Multi-agent orchestration architectures for real-time enterprise decision intelligence systems

The architectures that enable the coordination of distributed analytics, enterprise events, machine agents, human judgment, and governance constraints under low latency conditions are becoming more common for real-time enterprise decision intelligence systems. Multi-agent orchestration is an architectural approach that distributes intelligence across software agents, which perform the functions of sensing, reasoning, negotiating, executing and explaining tasks or actions, instead of having it centralized in a single analytical core. In this review, we focus on peer-reviewed journal articles published since 2015 that are directly relevant to multi-agent orchestration architecture in real-time enterprise decision intelligence systems. The literature reviewed shares a number of common themes: cyber-physical decentralization, event-driven analytics, hybrid human–AI decision structures, explainability, and digital-twin mediated coordination. Although reported studies demonstrate the benefits of agent-based orchestration in terms of adaptability, local responsiveness, and resilience, agent-based orchestration still faces several challenges, including interoperability, verification, cross-agent accountability, organizational integration, and empirical evaluation at enterprise scale. The field is important, because future decision intelligence systems demand predictive accuracy, in addition to coordinated, explainable, auditable, and context-sensitive decision execution in complex enterprise environments.